Computes the main effect of Factor A across levels of Factor B, analogous to the main effect in a factorial ANOVA.
Usage
lnRR_main(
data,
col_names = c("yi", "vi"),
append = TRUE,
method = "nakagawa",
Ctrl_mean,
Ctrl_sd,
Ctrl_n,
A_mean,
A_sd,
A_n,
B_mean,
B_sd,
B_n,
AB_mean,
AB_sd,
AB_n
)Arguments
- data
Data frame containing the variables used.
- col_names
Vector of two strings to name the output columns for the effect size and its sampling variance. Default is 'yi' and 'vi'.
- append
Logical. Append the results to
data. Default is TRUE- method
Method to compute lnRR. Can be either "nakagawa" or "morris". Default is "nakagawa".
- Ctrl_mean
Mean outcome from the Control treatment
- Ctrl_sd
Standard deviation from the control treatment
- Ctrl_n
Sample size from the control treatment
- A_mean
Mean outcome from the A treatment
- A_sd
Standard deviation from the A treatment
- A_n
Sample size from the A treatment
- B_mean
Mean outcome from the B treatment
- B_sd
Standard deviation from the B treatment
- B_n
Sample size from the B treatment
- AB_mean
Mean outcome from the interaction AxB treatment
- AB_sd
Standard deviation from the interaction AxB treatment
- AB_n
Sample size from the interaction AxB treatment
Value
A data frame containing the effect sizes and their sampling variance.
By default, the columns are named yi (effect size) and vi (sampling variance).
If append = TRUE, the results are appended to the input data; otherwise, only the computed effect size columns are returned.
References
Morris, W. F., Hufbauer, R. A., Agrawal, A. A., Bever, J. D., Borowicz, V. A., Gilbert, G. S., ... & Vázquez, D. P. (2007). Direct and interactive effects of enemies and mutualists on plant performance: a meta‐analysis. Ecology, 88(4), 1021-1029. https://doi.org/10.1890/06-0442
Lajeunesse, M. J. (2011). On the meta‐analysis of response ratios for studies with correlated and multi‐group designs. Ecology, 92(11), 2049-2055. https://doi.org/10.1890/11-0423.1
Macartney, E. L., Lagisz, M., & Nakagawa, S. (2022). The relative benefits of environmental enrichment on learning and memory are greater when stressed: A meta-analysis of interactions in rodents. Neuroscience & Biobehavioral Reviews, 135, 104554. https://doi.org/10.1016/j.neubiorev.2022.104554
Examples
# Example data for 2x2 factorial design (Fertilization x Warming)
data <- data.frame(
study_id = 1:2,
control_mean = c(10, 12), control_sd = c(2.0, 2.5), control_n = c(20, 18),
fertilization_mean = c(15, 16), fertilization_sd = c(2.2, 2.8), fertilization_n = c(20, 19),
warming_mean = c(11, 13), warming_sd = c(2.1, 2.6), warming_n = c(21, 17),
fert_warm_mean = c(17, 19), fert_warm_sd = c(2.4, 3.0), fert_warm_n = c(19, 20)
)
# Compute main effect of fertilization
result <- lnRR_main(
data = data,
Ctrl_mean = "control_mean", Ctrl_sd = "control_sd", Ctrl_n = "control_n",
A_mean = "fertilization_mean", A_sd = "fertilization_sd", A_n = "fertilization_n",
B_mean = "warming_mean", B_sd = "warming_sd", B_n = "warming_n",
AB_mean = "fert_warm_mean", AB_sd = "fert_warm_sd", AB_n = "fert_warm_n"
)